Haochang Shou

Non-GFR Determinants of Low-Molecular-Weight Serum Protein Filtration Markers in CKD

BACKGROUND Unlike the case with creatinine, conditions affecting the non-glomerular filtration rate (GFR) determinants of low-molecular-weight serum proteins, β-trace protein (BTP), β2-microglobulin (B2M), and cystatin C, are not well characterized. STUDY DESIGN Pooled cross-sectional analysis of 3 studies. SETTING & PARTICIPANTS 3,156 persons with chronic kidney disease from the MDRD (Modification of Diet in Renal Disease) Study, AASK (African American Study of Kidney Disease and Hypertension), and CRIC (Chronic Renal Insufficiency Cohort) Study. PREDICTORS Demographic and clinical factors hypothesized to be associated with non-GFR determinants of the filtration markers, selected from literature review and physiologic and clinical considerations. OUTCOMES Serum creatinine, BTP, B2M, and cystatin C levels. RESULTS In multivariable-adjusted errors-in-variables regression models that included adjustment for measured GFR (mGFR) and mGFR measurement error, creatinine level had stronger associations with male sex, black race, and higher urine creatinine excretion than the other filtration markers. BTP was associated less strongly with age, similar in direction with sex, and opposite in direction with race than creatinine level. Like cystatin C, B2M level was associated less strongly with age, sex, and race than creatinine level. BTP, B2M, and cystatin C levels were associated more strongly than creatinine level with other factors, including urine protein excretion and weight for BTP, smoking and urine protein excretion for B2M, and smoking for cystatin C. LIMITATIONS Findings may not be generalizable to populations without chronic kidney disease, and residual confounding with GFR due to incomplete adjustment for GFR measurement error. CONCLUSIONS Like creatinine, serum levels of low-molecular-weight proteins are affected by conditions other than GFR. Knowledge of these conditions can aid the interpretation of GFR estimates and risk using these markers and guide the use of these filtration markers in developing GFR estimating equations.

Structured functional principal component analysis

Motivated by modern observational studies, we introduce a class of functional models that expand nested and crossed designs. These models account for the natural inheritance of the correlation structures from sampling designs in studies where the fundamental unit is a function or image. Inference is based on functional quadratics and their relationship with the underlying covariance structure of the latent processes. A computationally fast and scalable estimation procedure is developed for high-dimensional data. Methods are used in applications including high-frequency accelerometer data for daily activity, pitch linguistic data for phonetic analysis, and EEG data for studying electrical brain activity during sleep.

Amyloid burden in cognitively normal elderly is associated with preferential hippocampal subfield volume loss.

The earliest sites of brain atrophy in Alzheimers disease are in the medial temporal lobe, following widespread cerebral cortical amyloid deposition. We assessed 74 cognitively normal participants with clinical measurements, amyloid-β-PET imaging, MRI, and a newly developed technique for MRI-based hippocampal subfield segmentation to determine the differential association of amyloid deposition and hippocampal subfield volume. Compared to amyloid-negative participants, amyloid-positive participants had significantly smaller hippocampal tail, presubiculum, subiculum, and total hippocampal gray matter volumes. We conclude that, prior to the development of cognitive impairment, atrophy in particular hippocampal subfields occurs preferentially with amyloid-β accumulation.

Electrocardiographic Measures and Prediction of Cardiovascular and Noncardiovascular Death in CKD

Limited studies have assessed the resting 12-lead electrocardiogram (ECG) as a screening test in intermediate risk populations. We evaluated whether a panel of common ECG parameters are independent predictors of mortality risk in a prospective cohort of participants with CKD. The Chronic Renal Insufficiency Cohort (CRIC) study enrolled 3939 participants with eGFR<70 ml/min per 1.73 m(2) from June 2003 to September 2008. Over a median follow-up of 7.5 years, 750 participants died. After adjudicating the initial 497 deaths, we identified 256 cardiovascular and 241 noncardiovascular deaths. ECG metrics were independent risk markers for cardiovascular death (hazard ratio, 95% confidence interval): PR interval ≥200 ms (1.62, 1.19-2.19); QRS interval 100-119 ms (1.64, 1.20-2.25) and ≥120 ms (1.75, 1.17-2.62); corrected QT (QTc) interval ≥450 ms in men or ≥460 ms in women (1.72, 1.19-2.49); and heart rate 60-90 beats per minute (1.21, 0.89-1.63) and ≥90 beats per minute (2.35, 1.03-5.33). Most ECG measures were stronger markers of risk for cardiovascular death than for all-cause mortality or noncardiovascular death. Adding these intervals to a comprehensive model of cardiorenal risk factors increased the C-statistic for cardiovascular death from 0.77 to 0.81 (P<0.001). Furthermore, adding ECG metrics to the model adjusted for standard risk factors resulted in a net reclassification of 12.1% (95% confidence interval 8.1%-16.0%). These data suggest common ECG metrics are independent risk factors for cardiovascular death and enhance the ability to predict death events in a population with CKD.

Statistical Methods for Cohort Studies of CKD: Survival Analysis in the Setting of Competing Risks

Survival analysis is commonly used to evaluate factors associated with time to an event of interest (e.g., ESRD, cardiovascular disease, and mortality) among CKD populations. Time to the event of interest is typically observed only for some participants. Other participants have their event time censored because of the end of the study, death, withdrawal from the study, or some other competing event. Classic survival analysis methods, such as Cox proportional hazards regression, rely on the assumption that any censoring is independent of the event of interest. However, in most clinical settings, such as in CKD populations, this assumption is unlikely to be true. For example, participants whose follow-up time is censored because of health-related death likely would have had a shorter time to ESRD, had they not died. These types of competing events that cause dependent censoring are referred to as competing risks. Here, we first describe common circumstances in clinical renal research where competing risks operate and then review statistical approaches for dealing with competing risks. We compare two of the most popular analytical methods used in settings of competing risks: cause-specific hazards models and the Fine and Gray approach (subdistribution hazards models). We also discuss practical recommendations for analysis and interpretation of survival data that incorporate competing risks. To demonstrate each of the analytical tools, we use a study of fibroblast growth factor 23 and risks of mortality and ESRD in participants with CKD from the Chronic Renal Insufficiency Cohort Study.

Cognitive behavioral therapy increases amygdala connectivity with the cognitive control network in both MDD and PTSD

Background Both major depressive disorder (MDD) and post-traumatic stress disorder (PTSD) are characterized by alterations in intrinsic functional connectivity. Here we investigated changes in intrinsic functional connectivity across these disorders as a function of cognitive behavioral therapy (CBT), an effective treatment in both disorders. Methods 53 unmedicated right-handed participants were included in a longitudinal study. Patients were diagnosed with PTSD (n = 18) and MDD (n = 17) with a structured diagnostic interview and treated with 12 sessions of manualized CBT over a 12-week period. Patients received an MRI scan (Siemens 3 T Trio) before and after treatment. Longitudinal functional principal components analysis (LFPCA) was performed on functional connectivity of the bilateral amygdala with the fronto-parietal network. A matched healthy control group (n = 18) was also scanned twice for comparison. Results LFPCA identified four eigenimages or principal components (PCs) that contributed significantly to the longitudinal change in connectivity. The second PC differentiated CBT-treated patients from controls in having significantly increased connectivity of the amygdala with the fronto-parietal network following CBT. Limitations Analysis of CBT-induced amygdala connectivity changes was restricted to the a priori determined fronto-parietal network. Future studies are needed to determine the generalizability of these findings, given the small and predominantly female sample. Conclusion We found evidence for the hypothesis that CBT treatment is associated with changes in connectivity between the amygdala and the fronto-parietal network. CBT may work by strengthening connections between the amygdala and brain regions that are involved in cognitive control, potentially providing enhanced top-down control of affective processes that are dysregulated in both MDD and PTSD.

Health effects of lesion localization in multiple sclerosis: spatial registration and confounding adjustment.

Brain lesion localization in multiple sclerosis (MS) is thought to be associated with the type and severity of adverse health effects. However, several factors hinder statistical analyses of such associations using large MRI datasets: 1) spatial registration algorithms developed for healthy individuals may be less effective on diseased brains and lead to different spatial distributions of lesions; 2) interpretation of results requires the careful selection of confounders; and 3) most approaches have focused on voxel-wise regression approaches. In this paper, we evaluated the performance of five registration algorithms and observed that conclusions regarding lesion localization can vary substantially with the choice of registration algorithm. Methods for dealing with confounding factors due to differences in disease duration and local lesion volume are introduced. Voxel-wise regression is then extended by the introduction of a metric that measures the distance between a patient-specific lesion mask and the population prevalence map.

Statistical Methods for Cohort Studies of CKD: Prediction Modeling

Prediction models are often developed in and applied to CKD populations. These models can be used to inform patients and clinicians about the potential risks of disease development or progression. With increasing availability of large datasets from CKD cohorts, there is opportunity to develop better prediction models that will lead to more informed treatment decisions. It is important that prediction modeling be done using appropriate statistical methods to achieve the highest accuracy, while avoiding overfitting and poor calibration. In this paper, we review prediction modeling methods in general from model building to assessing model performance as well as the application to new patient populations. Throughout, the methods are illustrated using data from the Chronic Renal Insufficiency Cohort Study.

Dysregulation of objectively assessed 24-hour motor activity patterns as a potential marker for bipolar I disorder: results of a community-based family study

There has been a growing number of studies that have employed actigraphy to investigate differences in motor activity in mood disorders. In general, these studies have shown that people with bipolar disorders (BPDs) tend to exhibit greater variability and less daytime motor activity than controls. The goal of this study was to examine whether patterns of motor activity differ in euthymic individuals across the full range of mood disorder subtypes (Bipolar I (BPI), Bipolar II (BPII) and major depression (MDD)) compared with unaffected controls in a community-based family study of mood spectrum disorders. Minute-to-minute activity counts derived from actigraphy were collected over a 2-week period for each participant. Prospective assessments of the level, timing and day-to-day variability of physical activity measures were compared across diagnostic groups after controlling for a comprehensive list of potential confounding factors. After adjusting for the effects of age, sex, body mass index (BMI) and medication use, the BPI group had lower median activity intensity levels across the second half of the day and greater variability in the afternoon compared with controls. Those with a history of BPII had increased variability during the night time compared with controls, indicating poorer sleep quality. No differences were found in the average intensity, variability or timing of activity in comparisons between other mood disorder subgroups and controls. Findings confirm evidence from previous studies that BPI may be a manifestation of a rhythm disturbance that is most prominent during the second half of the day. The present study is the largest study to date that included the full range of mood disorder subgroups in a nonclinical sample that increases the generalizability of our findings to the general community. The manifestations of activity patterns outside of acute episodes add to the accumulating evidence that dysregulation of patterns of activity may constitute a potential biomarker for BPD.

Cognitive Behavioral Therapy Is Associated With Enhanced Cognitive Control Network Activity in Major Depression and Posttraumatic Stress Disorder

BACKGROUND Both major depressive disorder (MDD) and posttraumatic stress disorder (PTSD) are characterized by depressive symptoms, abnormalities in brain regions important for cognitive control, and response to cognitive behavioral therapy (CBT). However, whether a common neural mechanism underlies CBT response across diagnoses is unknown. METHODS Brain activity during a cognitive control task was measured using functional magnetic resonance imaging in 104 participants: 28 patients with MDD, 53 patients with PTSD, and 23 healthy control subjects; depression and anxiety symptoms were determined on the same day. A patient subset (n = 31) entered manualized CBT and, along with controls (n = 19), was rescanned at 12 weeks. Linear mixed effects models assessed the relationship between depression and anxiety symptoms and brain activity before and after CBT. RESULTS At baseline, activation of the left dorsolateral prefrontal cortex was negatively correlated with Montgomery–Åsberg Depression Rating Scale scores across all participants; this brain–symptom association did not differ between MDD and PTSD. Following CBT treatment of patients, regions within the cognitive control network, including ventrolateral prefrontal cortex and dorsolateral prefrontal cortex, showed a significant increase in activity. CONCLUSIONS Our results suggest that dimensional abnormalities in the activation of cognitive control regions were associated primarily with symptoms of depression (with or without controlling for anxious arousal). Furthermore, following treatment with CBT, activation of cognitive control regions was similarly increased in both MDD and PTSD. These results accord with the Research Domain Criteria conceptualization of mental disorders and implicate improved cognitive control activation as a transdiagnostic mechanism for CBT treatment outcome.